Reference documentation and code samples for the Cloud AutoML V1 Client class ImageObjectDetectionModelMetadata.
Model metadata specific to image object detection.
Generated from protobuf message google.cloud.automl.v1.ImageObjectDetectionModelMetadata
Methods
__construct
Constructor.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ model_type |
string
Optional. Type of the model. The available values are: * |
↳ node_count |
int|string
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field. |
↳ node_qps |
float
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed. |
↳ stop_reason |
string
Output only. The reason that this create model operation stopped, e.g. |
↳ train_budget_milli_node_hours |
int|string
Optional. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual |
↳ train_cost_milli_node_hours |
int|string
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. |
getModelType
Optional. Type of the model. The available values are:
cloud-high-accuracy-1- (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.cloud-low-latency-1- A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.mobile-low-latency-1- A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.mobile-versatile-1- A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.mobile-high-accuracy-1- A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
| Returns | |
|---|---|
| Type | Description |
string | |
setModelType
Optional. Type of the model. The available values are:
cloud-high-accuracy-1- (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.cloud-low-latency-1- A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.mobile-low-latency-1- A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.mobile-versatile-1- A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.mobile-high-accuracy-1- A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
| Parameter | |
|---|---|
| Name | Description |
var |
string
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getNodeCount
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.
| Returns | |
|---|---|
| Type | Description |
int|string | |
setNodeCount
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.
| Parameter | |
|---|---|
| Name | Description |
var |
int|string
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getNodeQps
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
| Returns | |
|---|---|
| Type | Description |
float | |
setNodeQps
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
| Parameter | |
|---|---|
| Name | Description |
var |
float
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getStopReason
Output only. The reason that this create model operation stopped,
e.g. BUDGET_REACHED, MODEL_CONVERGED.
| Returns | |
|---|---|
| Type | Description |
string | |
setStopReason
Output only. The reason that this create model operation stopped,
e.g. BUDGET_REACHED, MODEL_CONVERGED.
| Parameter | |
|---|---|
| Name | Description |
var |
string
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getTrainBudgetMilliNodeHours
Optional. The train budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour. The actual
train_cost will be equal or less than this value. If further model
training ceases to provide any improvements, it will stop without using
full budget and the stop_reason will be MODEL_CONVERGED.
Note, node_hour = actual_hour * number_of_nodes_invovled.
For model type cloud-high-accuracy-1(default) and cloud-low-latency-1,
the train budget must be between 20,000 and 900,000 milli node hours,
inclusive. The default value is 216, 000 which represents one day in
wall time.
For model type mobile-low-latency-1, mobile-versatile-1,
mobile-high-accuracy-1, mobile-core-ml-low-latency-1,
mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train
budget must be between 1,000 and 100,000 milli node hours, inclusive.
The default value is 24, 000 which represents one day in wall time.
| Returns | |
|---|---|
| Type | Description |
int|string | |
setTrainBudgetMilliNodeHours
Optional. The train budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour. The actual
train_cost will be equal or less than this value. If further model
training ceases to provide any improvements, it will stop without using
full budget and the stop_reason will be MODEL_CONVERGED.
Note, node_hour = actual_hour * number_of_nodes_invovled.
For model type cloud-high-accuracy-1(default) and cloud-low-latency-1,
the train budget must be between 20,000 and 900,000 milli node hours,
inclusive. The default value is 216, 000 which represents one day in
wall time.
For model type mobile-low-latency-1, mobile-versatile-1,
mobile-high-accuracy-1, mobile-core-ml-low-latency-1,
mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train
budget must be between 1,000 and 100,000 milli node hours, inclusive.
The default value is 24, 000 which represents one day in wall time.
| Parameter | |
|---|---|
| Name | Description |
var |
int|string
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getTrainCostMilliNodeHours
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour.
Guaranteed to not exceed the train budget.
| Returns | |
|---|---|
| Type | Description |
int|string | |
setTrainCostMilliNodeHours
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour.
Guaranteed to not exceed the train budget.
| Parameter | |
|---|---|
| Name | Description |
var |
int|string
|
| Returns | |
|---|---|
| Type | Description |
$this | |